Mammography is an effective method of screening for breast cancer, a leading cause of mortality among women. However, analyzing mammograms requires skilled radiologists whose performance can be degraded by the demand of viewing a large number of images in a finite amount of time. A CAD feature has been provided in many mammography systems to assist radiologists in capturing true-positives (TP) that might otherwise have been overlooked. For example, see “Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks”, by Stelios Hakiotis et al., Signal Processing, Volume 87, Issue 7, July 2007.
Abnormalities visible in mammograms include microcalcifications (MCs), which are tiny deposits of calcium in breast carcinoma. It is very difficult to distinguish between malignant and benign microcalcification clusters (MCCs), even for experienced radiologists, which may lead to a high rate of unnecessary biopsies. Therefore, it is desirable to design the CAD algorithm in such a way that a high TP rate can be achieved while the number of false-positives (FPs) is kept to a minimum. It has been noted that many FP MCC candidates as selected by known mammography CAD systems were found to fall on linear normal structures such as blood vessels in digital or film-based mammograms. Thus, it has been recognized that removing those MCC candidates that are associated with linear structures will significantly reduce the overall FP rate.
Various methods for extracting linear structures from a mammographic image have been proposed. R. Zwiggelaar, T. C. Parr, and C. J. Taylor, in their article “Finding orientated line patterns in digital mammographic images,” Proc. 7th Br. Machine Vision Conf., 1996, pp. 715-724, have compared the performance of several different approaches (including orientated bin and line operator methods) to the detection of linear structures with synthetic mammographic images. Their results suggest significant differences between the different approaches. One approach has been implemented as a multi-scale line operator and gives intuitively convincing results. The output could be used for classifying linear structures.
The work of a line operator can be described as follows: The line operator takes the average grey level of pixels lying on an oriented local line passing through a target pixel and subtracts the average intensity of all the pixels in the locally oriented neighborhood. The line strength is compared for n orientations. Line direction is obtained from the orientation producing the maximum line strength. Scale information can be obtained by applying the line operator to images that are resealed by Gaussian smoothing and sub-sampling. For each pixel, the scale producing the maximum line strength is taken as the detected line scale.
N. Cemeaz and M. Brady, in their article “Finding Curvilinear Structures in Mammograms,” Lecture Notes in Computer Science, 905, pp. 372-382, 1995, introduced a method that estimates the intensity profile of curvilinear structures (CLS) in mammograms in a single scale. In this method, the CLS are assumed to have circular cross section when the breast is not compressed. And the cross section of CLS in a mammogram is assumed to be elliptical. Candidate pixels for CLS are detected using the response of a second order difference operation which is applied in four directions. If there is a sufficient high response for one of the orientations the pixel will form part of a CLS. A measure of line strength is obtained by determining the contrast of the line profile at these pixels.
Wai et al, in their article “A Multi-resolution CLS Detection Algorithm for Mammographic Image Analysis,” Medical Imaging Computing and Computer-Assisted Intervention, MICCAU, pp. 865-872, 2004′ adopted the two-step method from the work of Cemeaz and Brady and devised a multi-resolution ridge detector for structures ranging from 1800 microns to 180 microns. Wai et al also enhanced the performance of the detector by using a local energy thresholding to suppress undesirable response from noise. The local energy is also used to determine CLS junctions.
Alexander Schneider et al, in U.S. Patent Application Publication No. 2002/0159622, proposed a system and method for detecting lines in medical images. In their method, a direction image array and a line image array are formed by filtering a digital image with a single-peaked filter; convolving the regular array with second-order difference operators oriented along the horizontal, vertical and diagonal axes; and computing the direction image arrays and line image arrays as direct scalar functions of the results of the second order difference operations. As best understood by Applicants, line detection based on the use of four line operator functions requires fewer computations than line detection based on the use of three line operator functions, if the four line operator functions correspond to the special orientations of 0, 45, 90 and 135 degrees.
For the issue of FP reduction, Zhang et al, in their paper “A New False Positive Reduction Method for MCCs Detection in Digital Mammography,” Acoustics, Speech and Signal Processing 2001, Proc. IEEE Intl. Conf. on (ICASSP), V. 2, Issue 2001, pp. 1033-1036, 2001, described a mixed feature multistage FP reduction algorithm utilizing eleven features extracted from spatial and morphology domains. These features include gray-level description, shape description and clusters description; but no feature is directly related to linear structures. Wai et al mention in their article that the results from the multi-resolution ridge detector could be beneficial to microcalcification FP reduction but the realization of the reduction is absent. Moreover, Wai et al say that it is computationally inefficient to generate actual linear structures just for the purpose of confirming the association of an MCC candidate with a linear structure in mammography CAD.
There exists a need for an improved approach for image linear structure detection in mammography.